Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

Francesca Manni, Fons Van Der Sommen, Sveta Zinger, Esther Kho, Susan Brouwer De Koning, Theo Ruers, Caifeng Shan, Jean Schleipen, Peter H.N. De With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machine-learning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
Volume10951
ISBN (Electronic)9781510625495
DOIs
Publication statusPublished - 1 Jan 2019
EventSPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - Town and Country Resort & Convention Center, San Diego, United States
Duration: 16 Feb 201921 Feb 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10951
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging 2019
CountryUnited States
CitySan Diego
Period16/02/1921/02/19

Fingerprint

Tongue Neoplasms
tongue
Tumors
Squamous Cell Carcinoma
tumors
cancer
Neoplasms
Tissue
Imaging techniques
Inspection
Histology
Oncology
inspection
pharynx
Surgery
Support vector machines
Learning systems
Computer-Assisted Surgery
larynx
surgeons

Keywords

  • Cancer detection
  • Hyperspectral imaging
  • Image classification
  • Image-guided surgery
  • Intraoperative tumor detection
  • Support vector machine
  • Tongue cancer

Cite this

Manni, F., Van Der Sommen, F., Zinger, S., Kho, E., Brouwer De Koning, S., Ruers, T., ... De With, P. H. N. (2019). Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging. In B. Fei, & C. A. Linte (Eds.), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling (Vol. 10951). [109512K] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951). SPIE. https://doi.org/10.1117/12.2512238
Manni, Francesca ; Van Der Sommen, Fons ; Zinger, Sveta ; Kho, Esther ; Brouwer De Koning, Susan ; Ruers, Theo ; Shan, Caifeng ; Schleipen, Jean ; De With, Peter H.N. / Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging. Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. editor / Baowei Fei ; Cristian A. Linte. Vol. 10951 SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{c955006d2887444dae36a154a98a4023,
title = "Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging",
abstract = "Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machine-learning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94{\%}, specificity of 68{\%} and area under the curve (AUC) of 92{\%}. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.",
keywords = "Cancer detection, Hyperspectral imaging, Image classification, Image-guided surgery, Intraoperative tumor detection, Support vector machine, Tongue cancer",
author = "Francesca Manni and {Van Der Sommen}, Fons and Sveta Zinger and Esther Kho and {Brouwer De Koning}, Susan and Theo Ruers and Caifeng Shan and Jean Schleipen and {De With}, {Peter H.N.}",
year = "2019",
month = "1",
day = "1",
doi = "10.1117/12.2512238",
language = "English",
volume = "10951",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Baowei Fei and Linte, {Cristian A.}",
booktitle = "Medical Imaging 2019",
address = "United States",

}

Manni, F, Van Der Sommen, F, Zinger, S, Kho, E, Brouwer De Koning, S, Ruers, T, Shan, C, Schleipen, J & De With, PHN 2019, Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging. in B Fei & CA Linte (eds), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. vol. 10951, 109512K, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10951, SPIE, SPIE Medical Imaging 2019, San Diego, United States, 16/02/19. https://doi.org/10.1117/12.2512238

Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging. / Manni, Francesca; Van Der Sommen, Fons; Zinger, Sveta; Kho, Esther; Brouwer De Koning, Susan; Ruers, Theo; Shan, Caifeng; Schleipen, Jean; De With, Peter H.N.

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. ed. / Baowei Fei; Cristian A. Linte. Vol. 10951 SPIE, 2019. 109512K (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

AU - Manni, Francesca

AU - Van Der Sommen, Fons

AU - Zinger, Sveta

AU - Kho, Esther

AU - Brouwer De Koning, Susan

AU - Ruers, Theo

AU - Shan, Caifeng

AU - Schleipen, Jean

AU - De With, Peter H.N.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machine-learning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.

AB - Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machine-learning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.

KW - Cancer detection

KW - Hyperspectral imaging

KW - Image classification

KW - Image-guided surgery

KW - Intraoperative tumor detection

KW - Support vector machine

KW - Tongue cancer

UR - http://www.scopus.com/inward/record.url?scp=85068935866&partnerID=8YFLogxK

U2 - 10.1117/12.2512238

DO - 10.1117/12.2512238

M3 - Conference contribution

VL - 10951

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2019

A2 - Fei, Baowei

A2 - Linte, Cristian A.

PB - SPIE

ER -

Manni F, Van Der Sommen F, Zinger S, Kho E, Brouwer De Koning S, Ruers T et al. Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging. In Fei B, Linte CA, editors, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 10951. SPIE. 2019. 109512K. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512238